The iterative closest point (ICP) algorithm is a well-known approach to solving for pose-differences between clouds of points. It is used as an approach to transform LiDAR information in autonomous vehicles, to provide localization, tracking, and other parameters. ICP algorithms utilize the construct of nearest neighbors for identifying groups of points. A common technique utilized to implement the ICP algorithm is the k-d tree approach. Processing LiDAR data for real-time localization has proven difficult, in part because improvements in LiDAR resolution have been introduced, causing an increase in the amount of data needing to be processed. Improvements in hardware capabilities and computing processing speeds may not overcome the large quantities of data being collected within a prescribed time period. Some current LiDAR arrays can generate 220,000 points of data per rotation and operate at 10 rotations per second, e.g., 2.2 million data points per second that need to be processed in real-time.